{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import quandl\n",
    "spy_table = quandl.get('LSE/SPY5')\n",
    "amzn_table = quandl.get('WIKI/AMZN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "spy = spy_table.loc['2016',['Last Close']]\n",
    "amzn = amzn_table.loc['2016',['Close']]\n",
    "spy_log = np.log(spy['Last Close']).diff().dropna()\n",
    "amzn_log = np.log(amzn.Close).diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spy</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-22</th>\n",
       "      <td>-0.004462</td>\n",
       "      <td>-0.005543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>0.001372</td>\n",
       "      <td>-0.007531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.000946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.009081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>0.002086</td>\n",
       "      <td>-0.020172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 spy      amzn\n",
       "Date                          \n",
       "2016-12-22 -0.004462 -0.005543\n",
       "2016-12-23  0.001372 -0.007531\n",
       "2016-12-28  0.000928  0.000946\n",
       "2016-12-29 -0.005671 -0.009081\n",
       "2016-12-30  0.002086 -0.020172"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([spy_log,amzn_log],axis = 1).dropna()\n",
    "df.columns = ['spy','amzn']\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'amzn_return')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b031b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.scatter(df.spy,df.amzn)\n",
    "plt.xlabel('spx_return')\n",
    "plt.ylabel('amzn_return')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   amzn   R-squared:                       0.044\n",
      "Model:                            OLS   Adj. R-squared:                  0.040\n",
      "Method:                 Least Squares   F-statistic:                     10.63\n",
      "Date:                Wed, 23 May 2018   Prob (F-statistic):            0.00128\n",
      "Time:                        14:20:17   Log-Likelihood:                 608.99\n",
      "No. Observations:                 235   AIC:                            -1214.\n",
      "Df Residuals:                     233   BIC:                            -1207.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1.234e-05      0.001      0.010      0.992      -0.002       0.002\n",
      "spy            0.4921      0.151      3.261      0.001       0.195       0.789\n",
      "==============================================================================\n",
      "Omnibus:                       51.597   Durbin-Watson:                   2.255\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              570.651\n",
      "Skew:                           0.405   Prob(JB):                    1.22e-124\n",
      "Kurtosis:                      10.591   Cond. No.                         127.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "model = sm.ols(formula = 'amzn~spy',data = df).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pamameters:  Intercept    0.000012\n",
      "spy          0.492112\n",
      "dtype: float64\n",
      "residual:  Date\n",
      "2016-12-22   -0.003360\n",
      "2016-12-23   -0.008219\n",
      "2016-12-28    0.000477\n",
      "2016-12-29   -0.006303\n",
      "2016-12-30   -0.021211\n",
      "dtype: float64\n",
      "fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]\n"
     ]
    }
   ],
   "source": [
    "print('pamameters: ',model.params)\n",
    "print('residual: ', model.resid.tail())\n",
    "print('fitted values: ',model.predict()[-6:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d630588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,10))\n",
    "plt.scatter(df.spy,df.amzn)\n",
    "plt.xlabel('spx_return')\n",
    "plt.ylabel('amzn_return')\n",
    "plt.plot(df.spy,model.predict(),color = 'red')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
